Journal of Mining and Environement
Volume:10 Issue: 4, Autumn 2019

Auxiliary ventilation of the blind development heading in underground mines is one of the most challenging work activities amongst mining underground operations. The auxiliary forcing ventilation system provides positive pressure, cooling, controlling gas layering, and removing diesel fumes and dust levels from development headings, stopes, and services facilities. The effectiveness of the auxiliary forcing ventilation system depends upon many system variables. Currently, no scientific models and calculations are available that can be used to estimate the optimal distance from the outlet of the auxiliary forcing ventilation system to the development heading in underground mines that can provide the most efficient ventilation close to the face of the heading. In this work, scenarios are developed and simulated with a validated CFD model inside the ANSYS Fluent software. In each scenario, the system parameters such as dead zone, mean age of air, and face velocity are calculated, which are later used in the optimization process. By examining these parameters at the development heading zone, we can quantify the effectiveness of the ventilation system and confirm that the system design meets the government regulations. This work is carried out using the k-epsilon realizable turbulent model inside the ANSYS Fluent software.

In this work, the concentrations of the potentially toxic elements in stream sediments in SE Nigeria were assessed for pollution monitoring in mining, quarrying, and farming areas. The levels of iron, molybdenum, vanadium, copper, lead, zinc, nickel, cobalt, manganese, chromium, barium, and beryllium were determined. The concentrations of the elements were in the order of Fe > Ba > Mn > Cr > Zn > Pb > Cu > Co > Ni > As > Mo. There were significant positive correlations at P < 0.01 between Mo and Cu (r = 0.734), Mo and Pb (r = 0.811), and Cu and Pb (r = 0.836). The others were between Cu and V (r = 0.748), Pb and V (r = 0.793), Fe and V (r = 0.905), Fe and Be (r = 0.703), V and Be (r = 0.830), Cu and Pb (r = 0.778), and Fe and V (r = 0.905). The geoaccumulation index values were classified as polluted (0-1) to moderately polluted (1-2). The enrichment factors fell into moderate, significant, and very high enrichment. Pb, Co, and Ba attained significant enrichment factors. The potential ecological risk showed a strong risk level "C" in only three locations. Arsenic was a strong factor carrying risk. The potential ecological risk (Ei</sup>R) trend was Ei</sup>R (AS) > Ei</sup>R (Pb)> Ei</sup>R (Cu) > Ei</sup>R (Co) > Ei</sup>R (Cr) > Ei</sup>R (V) > Ei</sup>R (Ni) > Ei</sup>R (Zn).  Ba, Pb, and As should be monitored further to determine their source and recommend possible remedial measures. The result of this work could be used to improve water management efficiency and serve as a benchmark of vulnerability assessment of the studied area. This could also be useful for future impact assessment and adequate planning of mining and farming areas. In addition, the result obtained could assist the scientists to make appropriate environmental management strategies to reduce the influence of metal contamination triggered from the mining sites and farming areas both in Nigeria and globally.

Mining is among the oldest industries. It is the primary source of raw materials for most of the sectors. Little is known about the complex inter-sectoral carbon linkages of the mining industry. In this work, we estimate the inter- and intra-sectoral carbon linkage impacts of the mining sector across ten major economies by applying an input-output model, and the hypothetical extraction method and its modified version. The hypothetical extraction method removes an industrial block from an economic system, and afterwards, it makes a comparison between the before and after removal values. China with 195.47 Mt has the highest mining emissions, followed by USA, India, and Canada with 110.99 Mt, 108.79 Mt, and 76.92 Mt, respectively. The India’s mining sector with 26.33 t/104</sup> $ is the most carbon-intensive, followed by Japan and Canada with 6.84 t/104</sup> $ and 5.22 t/104</sup> $, respectively. China’s carbon emissions with -11.56% and -11.28%, respectively, have been affected the most by the total extraction of mining sector and forward carbon linkages, while for the backward carbon linkage, Canada with -1.33% has been affected the most. Canada has the highest mixed and internal emissions of 0.42 Mt and 47.88 Mt, respectively. However, China has the highest net-backward and net-forward emissions of 16.91 Mt and 189.22 Mt, respectively. For all nations, the mining sector is a net exporter of emissions to other industries. Based on the numerical findings, in this work, we discuss the mitigation measures for both the direct and indirect mining emissions.

Metals are ubiquitous within the earth crust. However, the exceptional high-level concentration of heavy metals in the soil due to natural or anthropogenic activities and the chemical forms in which they exist determine the level of risk they portend to the environment. This work was aimed at determining the background level of the presence of seven priority toxic metals (Cr, Ni, Pb, As, Cd, Cu, Zn) in the chemical phases of the overburden topsoil of a bituminous deposit prior to mining activities through the speciation analysis. The grab samples of overburden topsoil were initially obtained and homogenized to composites based on locations for the subsequent sequential extraction procedure (SEP). The specific physico-chemical properties of the sampled soils were simultaneously determined to complement the SEP inferential analysis. The results obtained showed that most metals were spatially bounded to the Fe-Mn oxides (reducible phase) followed by the organic (oxidizable) and the carbonates phases, respectively. Fractionally, the dominant soil texture in the studied area was sand (55.45%); however, the colloidal organic matter and Fe-Mn oxide phases played the dominant roles in the sorption activities of the selected metals. The soil chemical phase with the least metal pool was the exchangeable (water/salt) soluble fraction. The overall assessment revealed that the geogenic heavy metals in the topsoil posed no threats since a marginal fraction of the metals existed in the bio-available form in non-toxic concentrations in the order of Pb > Zn > Cu, while the potential mobility of metals showed that Zn was preferentially higher than Pb and Cu, respectively.

In this paper, we aim to achieve two specific objectives. The first one is to examine the applicability of the Artificial Neural Networks (ANNs) technique in ore grade estimation. Different training algorithms and numbers of hidden neurons are applied to estimate Cu grade of borehole data in the hypogene zone of porphyry copper-gold deposit, Masjed-Daghi, East Azerbaijan Province (Iran). The efficacy of ANNs in function-learning and estimation is compared with ordinary kriging (OK). As the kriging algorithms smooth the data, their applicability in the pre-processing of data for fractal analysis is not conducive. ANNs can be introduced as an alternative for this kind of problem. Secondly, we aim to delineate the potassic and phyllic alteration regions in the hypogene zone of Cu-Au porphyry deposit based on the estimation obtained by the ANNs and OK methods, and utilize the Concentration-Volume (C-V) fractal model. In this regard, at first, C-V log-log is generated based on the ANN results. The plots are then used to determine the Cu threshold values for the alteration zones. To investigate the correlation between the geological model and C-V fractal results, the log ratio matrix is applied. The results obtained show that Cu values less than 0.38% from ANNs have more overlapped voxels with phyllic alteration zone by an overall accuracy of 0.72. Spatial correlation between the potassic alteration zones resulting from 3D geological modeling and high concentration zones in C-V fractal model show that Cu values greater than 0.38% have more voxels overlapped with the potassic alteration zone by an overall accuracy of 0.76. Generally, the results obtained show that a combination of the ANNs and C-V fractal methods can be a suitable and robust tool for quantitative modeling of alteration zones instead of the qualitative methods.

Estimating the costs of blasting operations is an important parameter in open-pit mining. Blasting and rock fragmentation depend on two groups of variables. The first group consists of mass properties, which are uncontrollable, and the second one is the drill-and-blast design parameters, which can be controlled and optimized. The design parameters include burden, spacing, hole length, hole diameter, sub-drilling, charge weight, charge length, stemming length, and charge density. Blasting costs vary depending on the size of these parameters. Moreover, blasting brings about some undesirable results such as air overpressure, fly rock, back-break, and ground vibration. This paper proposes a mathematical model for estimating the costs of blasting operations in the Baghak gypsum mine. The cost of blasting operations in the objective function is divided into three parts: drilling costs, costs of blasting system, and costs of blasting labours. The decision variables used to minimize the costs include burden, spacing, hole diameter, stemming length, charge density, and charge weight. Constraints of the model include the boundary and operational limitations. Air overpressure in the mine is also anticipated as one of the model constraints. The non-linear model obtained with consideration of constraints is optimized by simulated annealing (SA). After optimizing the model by SA, the best values for the decision variables are determined. The value obtained for the cost was obtained to be equal to 2259 $ per 7700 tons for the desired block, which is less than the blasting costs in the Baghak gypsum mine.

The CERCHAR abrasivity test is very popular for determination of rock abrasivity. An accurate estimation of the CERCHAR abrasivity index (CAI) is useful for excavation operation costs. This paper presents a model to calculate CAI based on the gene expression programming (GEP) approach. This model is trained and tested based on a database collected from the experimental results available in the literature. The proposed GEP model predicts CAI based on two basic geomechanical properties of rocks, i.e. rock abrasivity index (RAI) and Brazilian tensile strength (BTS). Root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R2</sup>) are used to measure the model performance. Furthermore, the developed GEP model is compared with linear and non-linear multiple regression and other existing models in the literature. The results obtained show that GEP is a strong technique for the prediction of CAI.

The traditional approaches of modeling and estimation of highly skewed deposits have led to incorrect evaluations, creating challenges and risks in resource management. The low concentration of the rare earth element (REE) deposits, on one hand, and their strategic importance, on the other, enhances the necessity of multivariate modeling of these deposits. The wide variations of the grades and their relation with different rock units increase the complexities of the modeling of REEs. In this work, the Gazestan Magnetite-Apatite deposit was investigated and modeled using the statistical and geostatistical methods. Light and heavy REEs in apatite minerals are concentrated in the form of fine monazite inclusions. Using 908 assayed samples, 64 elements including light and heavy REEs from drill cores were analyzed. By performing the necessary pre-processing and stepwise factor analysis, and taking into account the threshold of 0.6 in six stages, a mineralization factor including phosphorus with the highest correlation was obtained. Then using a concentration-number fractal analysis on the mineralization factor, REEs were investigated in various rock units such as magnetite-apatite units. Next, using the sequential Gaussian simulation, the distribution of light, heavy, and total REEs and the mineralization factor in various realizations were obtained. Finally, based on the realizations, the analysis of uncertainty in the deposit was performed. All multivariate studies confirm the spatial structure analysis, simulation and analysis of rock units, and relationship of phosphorus with mineralization.

Providing a fresh and cool airflow in underground mines is one of the main concerns during mining. Destruction of support systems, the presence of undesirable objects in the airway and distortion of airflow are the parameters involved that would result in pressure loss, which would affect the ventilation network. There are a lot of research works about the ventilation network planning that consider the confidence in the planning but how reliable are these designs? These questions can be answered using the quantitative reliability evaluation. For the reliability evaluation of mine ventilation network, tunnel resistance and flow rate changes for all branches are considered as the reliability indices and criteria. This paper describes a stepwise method for evaluation of the underground coal mine network reliability associated with major losses using the cut set method. The reliability of the entire network is achieved by the reliability of every single component. The proposed model is implemented by the Takht coal mine. The Takht mine ventilation network probability of failure is in the range of 19-100% so reliability is in the range of 0-81% for the entire ventilation network.

Natural and artificial materials including rocks and cement-based materials such as concrete and cement mortar are affected both physically and chemically by various natural factors known as weathering factors. The freeze-thaw process, as a weathering factor, considerably affects the properties of rocks and concrete. Therefore, the effect of the freeze-thaw process on the physical and mechanical properties of materials should be taken into account in areas with the risk of this process. Given that few studies have been conducted on the effect of the freeze-thaw process on the fracture toughness, in this work, we aimed at investigating the effects of the freeze-thaw cycles and freezing temperature on the mode I and mode II fracture toughness of cement mortar. To this end, specimens were exposed to 0, 5, 10, 20, and 30 freeze-thaw cycles, and the mode I and mode II fracture toughness was determined in different cycles. The effect of freezing temperature in a freeze-thaw cycle on the mode I and mode II fracture toughness was also investigated. The damage factor was also defined based on the effective porosity of cement mortar, and its changes with the number of freeze-thaw cycles and mode I and mode II fracture toughness were studied. Finally, the decay function model provided by Mutluturk was investigated. According to the results obtained, the mode I and mode II fracture toughness of cement mortar decreased linearly with increase in the number of freeze-thaw cycles. The mode I and mode II fracture toughness decreased linearly with increase in the freezing temperature in a freeze-thaw cycle. The damage factor increased with increase in the number of freeze-thaw cycles, and, additionally, its relationship with mode I and mode II fracture toughness exhibited a linear behavior.

During an earthquake, the better performance of segmental tunnel lining, compared to the continuous in-cast concrete lining, is generally related to the joints between segments. In order to better understand the influence of the segment joints, their effect on the internal forces induced in tunnel lining simultaneously with the effects of the other influential parameters should be considered. In this work, the segmental joints were simulated by the representative stiffnesses and effects of these characteristics in relation to the other parameters such as the soil-liner interface behavior, number of segments in each ring and thickness of segments on the internal forces induced in structure were investigated. For this purpose, 2D numerical analyses were performed and the results obtained were discussed. Results showed that under the seismic condition, the components that had the most significant role on the internal axial forces induced in the segmental lining were rotational stiffness and axial stiffness of joints. Also the bending moments were more affected by the rotational stiffness. Generally, the radial joint stiffness had a less effect on the induced internal forces. With increase in the number of segments and their thickness, the effect of joint stiffness on the internal forces increases and the design of joints should be given more attention; however, the effects of joint stiffness and frictional behavior at the soil-liner interface on the maximum induced forces are almost independent from each other. Also in a specified joint behavior, by variation in each one of the other parameters including the soil-liner interface condition, number of segments and their thickness, the absolute magnitude of the maximum induced internal forces sometimes change significantly.

In layered and blocky rock slopes, toppling failure is a common mode of instability that may occur in mining engineering. If this type of slope failure occurs as a consequence of another type of failure, it is referred to as the secondary toppling failure. “Slide-head-toppling” is a type of secondary toppling failures, where the upper part of the slope is toppled as a consequence of a semi-circular sliding failure at the toe of the slope. In this research work, the slide-head-toppling failure is examined through a series of numerical modeling. Phase 2, as a software written based on the finite element method, is used in this work. Different types of slide-head-toppling failures including blocky, block-flexural, and flexural are simulated. A good agreement can be observed when the results of the numerical modeling are compared with those for the pre-existing physical modeling and analytical method.

The electrocoagulation/flotation process is a novel approach in mining industry that is implemented to return Cu metal to the production cycle, which improves copper recovery and reduces waste water. In this research work, the response surface methodology was applied to optimize the factors effective in Cu metal recovery and sludge volume produced from thickener overflow. To this end, the D-optimal experimental design was utilized. The influences of four independent parameters including the electrolysis time, initial pH, current density, and electrodes type were studied to investigate the initial Cu grade percentage (28%) and sludge volume produced from thickener overflow. All these parameters were found to have important effects on the Cu metal recovery and the sludge volume produced. The linear and quadratic models were utilized for the Cu grade and sludge volume, respectively. The importance of the independent variables and the interaction between them was assessed by ANOVA. The optimum operating conditions with 27.22% Cu grade were taken to be: electrolysis time: 6.5 min, initial pH: 6.7, current density: 50.2 A/m2</sup>, and electrode type: Fe-Al. Similarly, for the produced sludge volume of 861 cm3</sup>, the following conditions were found: electrolysis time: 15 min, initial pH: 4.1, current density: 48.7, and electrode type: Fe-Al. The outcomes underscored a practical viewpoint of electrocoagulation, known as an acceptable method for Cu recovery from mine industrials, especially in mineral processing plants.

According to the wide application of segmental lining in mechanized tunneling, recognizing the behavior of segmental lining joints is important in tunnels designing. In the structural analysis of the tunnel segmental lining, segmental joints can be considered as elastic joints, and their stiffness characteristics are affected by the rotational, shear, and axial stiffness. The purpose of this work is to investigate the effect of the rotational, shear, and axial stiffness of segmental lining joints on the internal forces (bending moment and axial force)under the static conditions. For this purpose, a 3D numerical analysis was carried out using the ABAQUS software. The results obtained show that by increasing the rotational stiffness of the segmental joint, the bending moment increases, and for lower values of rotational stiffness, the bending moment variations are higher, while the axial force variations are very slight in comparison with the bending moment. By increasing the axial and shear stiffness of the segmental joint, changes of the bending moment and axial force in segmental lining are negligible.

The appropriate operating of mining machines is affected by both the executive and environmental factors. Considering the effects and the related risks lead to a better understanding of the failures of such machines. This leads to a proper prediction of the reliability parameters of such machines. In this research work, the reliability and maintainability analysis of the loading and haulage machines in the Sungun Copper Mine, considering the repair condition as multiple repairable units, was performed. For this purpose, the data necessary for the loading and haulage equipment including 2 loaders and 8 dump trucks for a 15-month period was collected and categorized in 10 operational units after the system and sub-systems of the department were determined. Initially, the time between failures (TBFs) and time to repair (TTR) for each unit was calculated. Then 20 sub-systems were developed. Primarily, the Stata software was utilized to carry out the heterogeneity test for all the sub-systems. In consequence, most of the sub-systems were regarded as the heterogeneous ones, except for 7 of them including the dump truck units 1, 2, 3, 4, 5, 7, and 8 in TBFs. Hence, "PHM" that is a covariate-based model displayed the heterogeneous group. Its reliability function was also estimated. For the next step, the trend tests were done on the non-heterogeneous sub-systems by means of the Minitab software. The homogeneous sub-systems with failure trend were modeled by “NHPP”. Afterwards, the non-trended sub-systems formed the data group. Later, the correlation tests were modeled by “HPP”. Finally, the reliability and maintainability functions were calculated with the 95% confidence level.

This work aims at figuring out the spatial relationships between the geophysical and geological models in a case study pertaining to copper-sulfide mineralization through an integrated 3D analysis of favorable target. The Ghalandar Skarn-Porphyry Cu Deposit, which is located in NW Iran, is selected for this research work. Three geophysical surveys of direct current electrical resistivity and induced polarization tomography along with magnetometry are performed to construct the physical properties of electrical resistivity, chargeability, and magnetic susceptibility, respectively. Inverse modeling and geostatistical interpolation are utilized to generate the physical 3D models. A 3D model of Cu grade is generated using ordinary kriging; however, the indicator kriging method is run to design a 3D model of rock types through incorporating the drilling results. Block models of geophysical and geological characteristics are cast in a similar 3D mesh to investigate their relationships in copper mineralization. A concentration-volume multi-fractal method is utilized to divide each model into its sub-sets, where the most productive portions in association with Cu-bearing mineralization are distinguished. Note that sub-sets of geophysical models are spatially matched with geological models of Cu grade and rock types. The zones with low electrical resistivity, high chargeability, and low magnetic susceptibility correspond to the main source of Cu mineralization in a dominated skarn rock type setting.

In this research work, solvent extraction and stripping of zinc ions from a Zn-Mn-Cd-bearing solution was investigated using D2EHPA as the extractant in a chloride medium. The efficiency of the extraction and stripping stages was evaluated separately, and different parameters such as the pH, extractant concentration, reaction temperature, and contact time were studied. Based on the results obtained, 97% of zinc, 14% of manganese, and 3% of cadmium were extracted at pH = 2.5, 10% (v/v) of D2EHPA, and 40 °C from the solution containing 5 g L-1</sup> of each metal ion. The stripping isotherms of zinc, manganese, and cadmium at different pH values showed that manganese and zinc were stripped at two different pH values. Thus more than 70% of manganese and more than 90% of zinc were stripped at pH = 2.5 and pH = 0.5, respectively. Kinetic studies indicated that the extraction and stripping of zinc in the first 0.5-1 minute was high. The McCabe–Thiele diagrams showed that two stages of extraction and two stages of stripping in the continuous counter-current flow condition were adequate to separate zinc from Mn and Cd. The dominant Zn species extracted by D2EHPA was ZnCl+</sup>, and the values for the thermodynamic parameters ΔHo</sup>, ΔSo</sup>, and ΔGo</sup> were 25.65 kJ mol−1</sup>, 79.20 J K−1</sup> mol−1</sup>, and 0.86 kJ mol−1</sup>, respectively, which showed that the reaction was endothermic at equilibrium.

In this paper, we present an integrated model to find the optimum size of blast block that uses (i) a multi-criteria decision-making method to specify the applicable size of the mineable block; (ii) a linear programming method for the selection of the blasted areas to be excavated and in deciding the quantity of ores and wastes to be mined from each one of the selected blocks. These two methods use improved estimates of the orebody characteristics utilizing the blast hole data in addition to the usual borehole statistics to improve the prediction accuracy of the block level ore body characteristics. This work aims to make a mathematical model to figure out the ideal width and length of the blast block in order to curtail drilling and blasting expenses in open-pit mines. As a consequence, the effective blast block size is heeded so as to decrease the expenses of drilling and blasting. Furthermore, a complete set of actual principles is presented to specify the applicable size of the mineable block by means of the multi-criteria decision-making method of fuzzy logic. The aforementioned model is practiced to forecast the block size necessary for the purpose of production planning. Next, a mixed integer programming model is developed to blast planning in order to select the optimal size of the blast block by considering the mineable block. The proposed model is applied in the Chadormalu iron ore mine and the rationality of the model is demonstrated by the outcomes of dissimilar circumstances.

Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. The Group Method of Data Handling (GMDH) type of neural network and Radial Basis Function (RBF) neural network, as two kinds of the soft computing method, are powerful tools for identifying and assessing the unpredicted and uncertain conditions. Hence, this work aims to develop prediction models for estimating the production rate of chain saw machines using the RBF neural network and GMDH type of neural network, and then to compare the results obtained from the developed models based on the performance indices including value account for, root mean square error, and coefficient of determination. For this purpose, the parameters of 98 laboratory tests on 7 carbonate rocks are accurately investigated, and the production rate of each test is measured. Some operational characteristics of the machines, i.e. arm angle, chain speed, and machine speed, and also the three important physical and mechanical characteristics including uniaxial compressive strength, Los Angeles abrasion test, and Schmidt hammer (Sch) are considered as the input data, and another operational characteristic of the machines, i.e. production rate, is considered as the output dataset. The results obtained prove that the developed GMDH model is able to provide highly promising results in order to predict the production rate of chain saw machines based on the performance indices.

Nowadays, Barton’s Joint Roughness Coefficients (JRC) are widely used as the index for roughness and as a challenging fracture property. When JRC ranking is the goal, deriving JRC from different fractal/wavelet procedures can be conflicting. Complexity increases when various rankings outcome from different calculation methods. Therefore, using Barton’s JRC, we cannot make a decision based on the proven mathematical theories because each method has a different rank. Ideally, these rankings must be equal but, in practice, they are different for each method. To solve this problem and to achieve a robust and valid ranking for JRC, Condorcetand Borda count methods have been used. These methods have been proposed as fusion approaches. Re-ranking of JRC using different methods integrated with Condorcet showed confusion in ranking of the JRC4, JRC5, and JRC6 profiles. This ambiguity is equal to equalizing decision conditions about all the three at the examination of the winners, losers, and draws in pairwise matrices. Therefore, Borda Count was applied and resulted in robust rankings. In fact, a new approach for a roughness measurement is presented. A new JRC ranking called JRCN is introduced. This new ranking shows a lower sum of squared errors (0.00390) in comparison with the original JRC ranking method (0.00410) and ranked JRCN1</sub> to JRCN10</sub>. Thus it is proposed to consider JRCN as a new and improved version of JRC rankings.